Remerciez-le!

Remerciez @Admin pour avoir partagé cet document gratuitement, de la manière la plus simple, en partageant sur les réseaux sociaux.

The Egyptian Int. J. of Eng. Sci. and Technology Vol.14, No. 3 (Sept. 2011) AN

The Egyptian Int. J. of Eng. Sci. and Technology Vol.14, No. 3 (Sept. 2011) AN INTEGRATED APPROACH FOR MULTI-OBJECTIVE PRODUCTION AND DISTRIBUTION PLANNING IN SUPPLY CHAIN MANAGEMENT* Mohamed A. El-baz1 and Yehya. I. Mesalam+ Industrial Eng. Dept., Zagazig University, Zagazig, Egypt ABSTRACT In real world, supply chain works as interrelating network of suppliers, manufacturers, distributers, and customers to satisfy customer satisfaction. Because of the nature of the conflicting objective functions that affect the performance of supply chain management, coordinate all phases of supply chain as a whole should be considered. This paper presents the solutions for integrated production and distribution planning problem and investigates the effectiveness of their integration through a computational study. The environment is a multi-plant, multi-retailers, multi-item and multi-period where the objective functions are total cost, delivery time, and quality. The model is implemented using Fuzzy Linear Programming (FLP), Goal Programming (GP), and Fuzzy Goal Programming (FGP) approaches. The results obtained by using lingo 11.0 software show that the proposed FGP provides high quality solution as well as the effectiveness of the integrated model over the decoupled one. KEY WORDS: supply chain, production distribution planning, multi-objective, integrated approach, fuzzy goal programming. UNE APPROCHE INTÉGRÉE POUR PLUSIEURS OBJECTIF DE PRODUCTION ET PLANIFICATION DE LA DISTRIBUTION EN SUPPLY CHAIN MANAGEMENT RÉSUMÉ Dans le monde réel, chaîne d'approvisionnement fonctionne comme un réseau interrelation des fournisseurs, fabricants, distributeurs et clients à satisfaire la satisfaction du client. En raison de la nature des fonctions objectives contradictoires qui affectent les performances de la gestion de chaîne d'approvisionnement, de coordonner toutes les phases de la chaîne logistique dans son ensemble doit être envisagée. Ce document présente les solutions pour la production intégrée et problème de planification de distribution et examine l'efficacité de leur intégration à travers une étude computationnelle. L'environnement est un multi-site, multi-détaillants multi-point et multi-période où les fonctions objectives sont le coût total, délai de livraison et de qualité. Le modèle est implémenté en utilisant Fuzzy Programmation Linéaire (FLP), la programmation Objectif (GP) et Fuzzy Goal Programming (FGP) approches. Les résultats obtenus en utilisant le logiciel montrent que le jargon de 11,0 FGP proposé offre une solution de haute qualité ainsi que l'efficacité du modèle intégré sur le découplage. MOTS CLES: chaîne d'approvisionnement, planification de la distribution de production, multi-objectifs, l'approche intégrée, la programmation objective floue. * Received: 14/8/2011, Accepted: 11/9/2011 (Original Paper) + Contact Author (e-mail: ymesalam@yahoo.com) 1 Alternative author (e-mail: elbaz@mail2world.com ) EIJEST 396 AN INTEGRATED APPROACH FOR MULTI-OBJECTIVE PRODUCTION AND DISTRIBUTION PLANNING IN SUPPLY CHAIN MANAGEMENT El-baz and Mesalam 1. INTRODUCTION Supply chain management (SCM) has been a hot topic in the management arena in the recent years. The term “supply chain” (SC) conjures up images of products, or supplies, moving from manufacturers to distributors to retailers to customers, along a chain, in order to fulfill a customer request (Gong et al. 2008). SCM explicitly recognizes interdependencies and requires effective relationship management between chains. The challenge in global SCM is the development of decision-making frameworks that accommodate diverse concerns of multiple entities across the supply chain. Considerable efforts have been expended in developing decision models for SC problems (Narasimhan and Mahapatra, 2004). Enterprises have to satisfy customers with a high service level during standing high transportation, raw material and distribution costs. In traditional SCs, purchasing, production, distribution, planning and other logistics functions are handled independently by decision makers although SCs have different objectives. To overcome global risks in related markets, decision makers are obliged to fix a mechanism which different objective functions (minimizing transportation/production, backorder, holding, purchasing costs and maximizing profit and customer service level etc.) can be integrated together. SCs performance measures are categorized as qualitative and quantitative. Customer satisfaction, flexibility, and effective risk management belong to qualitative performance measures. Quantitative performance measures are also categorized by: (1) objectives that are based directly on cost or profit such as cost minimization, sales maximization, profit maximization, etc. and (2) objectives that are based on some measure of customer responsiveness such as fill rate maximization, customer response time minimization, lead time minimization, etc (Altiparmak et al., 2006). However, the SCM design and planning is usually involving trade-offs among different goals. In this study, we developed a Mixed Integer Linear Programming model (MILP) to design and optimize a supply chain network via providing multi objective functions mentioned above together. We considered three objectives for SCM problem: (1) Minimization of total costs. (2) Maximize the utilization level of facilities. (3) Minimization of total delivery time. The following details the organization of the remainder of this paper. Section 2 is dedicated to a review of the literature. Section 3 describes the proposed fuzzy model. Section 4 develops the mathematical model and the decoupled one and the integrated model. Section 5 presents an illustrative case study for implementing the feasibility of applying the proposed approach. Finally, Section 6 presents the conclusions and recommendations. 2. LITERATURE REVIEW In real-world situations, most enterprises have only paid attention on separately optimizing their production/distribution planning decisions, but using these decisions prevent possible improvement in decision effectiveness. Hence, the issues of how to simultaneously integrate manufacturing and distribution systems in a supply chain with multi objectives have attracted considerable interest from both practitioners and academics (Liang and Cheng, 2009). Literature review presents a review about supply chain modeling and fuzzy applications in SC planning, respectively. Haq et al. (1991) considered a single product, a three stages SC with one production facility and multi production steps, several warehouses, and several retailers. In 2002, Lee and Kim also developed almost similar model for production and distribution problem on SC structure. The model addressed problem of multi shop production which produced different products, with stack buffers to temporarily store the product, and intermediate warehouses and retailers. Both researches determined the quantity produced at each production stage, quantity transported from each stage to the next stage of SC, and inventory level at all SC stages, but with different formulation. Furthermore, Haq et al. (1991) also considered realistic condition during model development, such as setup time, production lead time, distribution lead time, losses during production and distribution, recycling of production losses, and backlogging. In other paper, Barbarosoglu and Ozgur (1999) considered a three stages SC, a single plant producing multiple products which are distributed to several depots and delivered from depots to customers. They formulated an integrated model to determine the quantity produced and quantity transported from plant to depot, quantity transported from depot to customer, and inventory 397 The Egyptian Int. J. of Eng. Sci. and Technology Vol.14, No. 3 (Sept. 2011) level at each plant and depot. In a multi-plant production system with several product items, the assignment of productions to plants determines the production performance. Moreover, in such a production system with scattered customers the assignment of plants to customers for distribution determines the performance of distribution. Integration of these two functions may lead to a substantial saving in global costs and to an improvement in relevant service by exploiting scale economies of production and transportation, balancing production lots and vehicle loads, and reducing total inventory and stock out (Fumero and Vercellis 1999). Martin et al. (1993) and Thomas and Griffin (1996) provided evidence of the potential economic benefits derived from an integration of production and distribution planning. Cohen and Lee (1989) present a deterministic, mixed integer, non-linear programming with economic order quantity technique to develop a global supply chain plan. Output of the model provides global resource deployment policy for the plants, distribution centers and customer zones. Pyke and Cohen (1993) develop a mathematical programming model by using stochastic sub-models to design an integrated supply chain that involves manufacturers, warehouses and retailers. Due to not considering multiple products and having only one plant, one warehouse and one retailer, the model fails to represent the complicated supply chain networks of the real world. Ozdamar and Yazgac (1997) develop a distribution/production system that involves a manufacturer center and its warehouses. The paper tries to minimize total costs such as inventory; transportation costs etc. under production capacity and inventory equilibrium constraints. Syarif et al. (2002) propose new algorithm based genetic algorithm to design a multi stage supply chain distribution network under capacity constraints for each echelon. Although experimental results show that the proposed algorithm can give better heuristic solutions than traditional genetic algorithm, the comparison with the performances of other meta-heuristic techniques (tabu search, simulated annealing etc.) are remained unanswered. Yan et al. (2003) try to contrive a network which involves suppliers, manufacturers, distribution centers and customers via a mixed integer programming under logic and material requirements constraints. Chen and Lee (2004) develop a multi-product, multi-stage, and multi-period scheduling model to deal with multiple incommensurable goals for a multi-echelon supply chain network with uncertain market demands and product prices. The supply chain scheduling model is constructed as a mixed-integer nonlinear programming problem to satisfy several conflict objectives, such as fair profit distribution among all participants, safe inventory levels, maximum customer service levels, and robustness of decision to uncertain product demands, therein the compromised uploads/Industriel/eijest-volume-14-issue-eijest-vol-14-2011-pages-396-410.pdf